Averaged dependence estimators for DoS attack detection in IoT networks
Wireless sensor networks (WSNs) have evolved to become an integral part of the contemporary Internet of Things (IoT) paradigm. The sensor node activities of both sensing phenomena in their immediate environments and reporting their findings to a centralized base station (BS) have remained a core pla...
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| Published in: | Future generation computer systems Vol. 102; pp. 198 - 209 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.01.2020
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| ISSN: | 0167-739X, 1872-7115 |
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| Abstract | Wireless sensor networks (WSNs) have evolved to become an integral part of the contemporary Internet of Things (IoT) paradigm. The sensor node activities of both sensing phenomena in their immediate environments and reporting their findings to a centralized base station (BS) have remained a core platform to sustain heterogeneous service-centric applications. However, the adversarial threat to the sensors of the IoT paradigm remains significant. Denial of service (DoS) attacks, comprising a large volume of network packets, targeting a given sensor node(s) of the network, may cripple routine operations and cause catastrophic losses to emergency services. This paper presents an intelligent DoS detection framework comprising modules for data generation, feature ranking and generation, and training and testing. The proposed framework is experimentally tested under actual IoT attack scenarios, and the accuracy of the results is greater than that of traditional classification techniques.
•DoS attack detection for IoT platforms.•AODE-based classification of network traffic.•Machine learning and applications for network security in IoT on 5G networks. |
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| AbstractList | Wireless sensor networks (WSNs) have evolved to become an integral part of the contemporary Internet of Things (IoT) paradigm. The sensor node activities of both sensing phenomena in their immediate environments and reporting their findings to a centralized base station (BS) have remained a core platform to sustain heterogeneous service-centric applications. However, the adversarial threat to the sensors of the IoT paradigm remains significant. Denial of service (DoS) attacks, comprising a large volume of network packets, targeting a given sensor node(s) of the network, may cripple routine operations and cause catastrophic losses to emergency services. This paper presents an intelligent DoS detection framework comprising modules for data generation, feature ranking and generation, and training and testing. The proposed framework is experimentally tested under actual IoT attack scenarios, and the accuracy of the results is greater than that of traditional classification techniques.
•DoS attack detection for IoT platforms.•AODE-based classification of network traffic.•Machine learning and applications for network security in IoT on 5G networks. |
| Author | Sanguanpong, Surasak So-In, Chakchai Firdous, Syed Naeem Vo, Van Nhan Nguyen, Tri Gia Baig, Zubair A. |
| Author_xml | – sequence: 1 givenname: Zubair A. surname: Baig fullname: Baig, Zubair A. organization: School of Information Technology, Deakin University, Geelong, Victoria, 3220, Australia – sequence: 2 givenname: Surasak surname: Sanguanpong fullname: Sanguanpong, Surasak organization: Department of Computer Engineering, Faculty of Engineering, Kasetsart University, 10900, Thailand – sequence: 3 givenname: Syed Naeem surname: Firdous fullname: Firdous, Syed Naeem organization: Edith Cowan University, Perth, 6000, Australia – sequence: 4 givenname: Van Nhan surname: Vo fullname: Vo, Van Nhan organization: Applied Network Technology (ANT) Laboratory, Department of Computer Science, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand – sequence: 5 givenname: Tri Gia surname: Nguyen fullname: Nguyen, Tri Gia organization: Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Viet Nam – sequence: 6 givenname: Chakchai surname: So-In fullname: So-In, Chakchai email: chakso@kku.ac.th organization: Applied Network Technology (ANT) Laboratory, Department of Computer Science, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand |
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| Title | Averaged dependence estimators for DoS attack detection in IoT networks |
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